

import unittest
from pathlib import Path
from tempfile import TemporaryDirectory

from transformers import AutoConfig, TFAutoModel, is_tensorflow_text_available, is_tf_available
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.testing_utils import require_tensorflow_text, require_tf, slow


if is_tf_available():
			import tensorflow as tf

if is_tensorflow_text_available():
			from transformers.models.bert import TFBertTokenizer


a__												=						['bert-base-uncased', 'bert-base-cased']
a__												=						'hf-internal-testing/tiny-bert-tf-only'

if is_tf_available():

			class 							UpperCAmelCase_						(     tf.keras.Model  ):
										"""simple docstring"""







										def __init__(     self			,				_a			)       ->       int:
															super().__init__()
															_a      :       Any   =						tokenizer
															_a      :       Union[str, Any]   =						AutoConfig.from_pretrained(_a			)
															_a      :       Dict   =						TFAutoModel.from_config(_a			)




										def 							__lowercase							(     self			,				_a			)       ->       Any:
															_a      :       int   =						self.tokenizer(_a			)
															_a      :       int   =						self.bert(**_a			)
															return out["pooler_output"]

@require_tf
@require_tensorflow_text
class 							UpperCAmelCase_						(     unittest.TestCase  ):
							"""simple docstring"""







							def 							__lowercase							(     self			)       ->       str:
												super().setUp()

												_a      :       Dict   =						[
												    BertTokenizer.from_pretrained(_a			) for checkpoint in (TOKENIZER_CHECKPOINTS * 2)
												]  # repeat for when fast_bert_tokenizer=false
												_a      :       List[Any]   =						[TFBertTokenizer.from_pretrained(_a			) for checkpoint in TOKENIZER_CHECKPOINTS] + [
												    TFBertTokenizer.from_pretrained(_a			,				use_fast_bert_tokenizer=_a			)
												    for checkpoint in TOKENIZER_CHECKPOINTS
												]
												assert len(self.tokenizers			) == len(self.tf_tokenizers			)

												_a      :       Dict   =						[
												    "This is a straightforward English test sentence.",
												    "This one has some weird characters\rto\nsee\r\nif  those\u00E9break things.",
												    "Now we're going to add some Chinese: 一 二 三 一二三",
												    "And some much more rare Chinese: 齉 堃 齉堃",
												    "Je vais aussi écrire en français pour tester les accents",
												    "Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ",
												]
												_a      :       Dict   =						list(zip(self.test_sentences			,				self.test_sentences[::-1]			)			)


							def 							__lowercase							(     self			)       ->       int:
												for tokenizer, tf_tokenizer in zip(self.tokenizers			,				self.tf_tokenizers			):
																	for test_inputs in (self.test_sentences, self.paired_sentences):
																						_a      :       str   =						tokenizer(_a			,				return_tensors='''tf'''			,				padding='''longest'''			)
																						_a      :       Any   =						tf_tokenizer(_a			)

																						for key in python_outputs.keys():
																											self.assertTrue(tf.reduce_all(python_outputs[key].shape == tf_outputs[key].shape			)			)
																											self.assertTrue(tf.reduce_all(tf.cast(python_outputs[key]			,				tf.intaa			) == tf_outputs[key]			)			)


							@slow
							def 							__lowercase							(     self			)       ->       Any:
												for tf_tokenizer in self.tf_tokenizers:
																	_a      :       List[str]   =						tf_tokenizer(self.paired_sentences			)
																	_a      :       Union[str, Any]   =						tf_tokenizer(
																	    text=[sentence[0] for sentence in self.paired_sentences]			,				text_pair=[sentence[1] for sentence in self.paired_sentences]			,				)
																	for key in merged_outputs.keys():
																						self.assertTrue(tf.reduce_all(tf.cast(merged_outputs[key]			,				tf.intaa			) == separated_outputs[key]			)			)


							@slow
							def 							__lowercase							(     self			)       ->       Optional[int]:
												for tf_tokenizer in self.tf_tokenizers:
																	_a      :       Optional[Any]   =						tf.function(_a			)
																	for test_inputs in (self.test_sentences, self.paired_sentences):
																						_a      :       Tuple   =						tf.constant(_a			)
																						_a      :       List[str]   =						compiled_tokenizer(_a			)
																						_a      :       Any   =						tf_tokenizer(_a			)

																						for key in eager_outputs.keys():
																											self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key]			)			)




							@slow
							def 							__lowercase							(     self			)       ->       Tuple:
												for tf_tokenizer in self.tf_tokenizers:
																	_a      :       str   =						ModelToSave(tokenizer=_a			)
																	_a      :       List[Any]   =						tf.convert_to_tensor(self.test_sentences			)
																	_a      :       Optional[int]   =						model(_a			)  # Build model with some sample inputs
																	with TemporaryDirectory() as tempdir:
																						_a      :       Optional[Any]   =						Path(_a			) / "saved.model"
																						model.save(_a			)
																						_a      :       List[Any]   =						tf.keras.models.load_model(_a			)
																	_a      :       Optional[Any]   =						loaded_model(_a			)
																	# We may see small differences because the loaded model is compiled, so we need an epsilon for the test
																	self.assertLessEqual(tf.reduce_max(tf.abs(out - loaded_output			)			)			,				1e-5			)





from __future__ import annotations

from math import pi
from typing import Protocol

import matplotlib.pyplot as plt
import numpy as np




class 					lowerCamelCase__				(							__lowercase):




	'''simple docstring'''




	def   _lowerCamelCase							(   self						:Tuple	,  a						:float		)						->					float:
								return 0.0




def       _SCREAMING_SNAKE_CASE   (						_lowerCamelCase					:     np.ndarray		,    _lowerCamelCase					:     int)   ->       tuple[int | float, int | float]:






							'''simple docstring'''





							__UpperCamelCase      :  List[str]       =   min([-20, np.min(fft_results[1 : samplerate // 2 - 1])])
							__UpperCamelCase      :  Any       =   max([20, np.max(fft_results[1 : samplerate // 2 - 1])])
							return lowest, highest




def       _SCREAMING_SNAKE_CASE   (						_lowerCamelCase					:     FilterType		,    _lowerCamelCase					:     int)   ->       None:






							'''simple docstring'''





							__UpperCamelCase      :  List[str]       =   512
							__UpperCamelCase      :  List[Any]       =   [1] + [0] * (size - 1)
							__UpperCamelCase      :  List[Any]       =   [filter_type.process(_lowerCamelCase) for item in inputs]

							__UpperCamelCase      :  Optional[Any]       =   [0] * (samplerate - size)  # zero-padding
							outputs += filler
							__UpperCamelCase      :  Optional[int]       =   np.abs(np.fft.fft(_lowerCamelCase))
							__UpperCamelCase      :  Optional[int]       =   20 * np.logaa(_lowerCamelCase)

							# Frequencies on log scale from 24 to nyquist frequency
							plt.xlim(24		,    samplerate / 2 - 1)
							plt.xlabel("Frequency (Hz)")
							plt.xscale("log")

							# Display within reasonable bounds
							__UpperCamelCase      :  Optional[Any]       =   get_bounds(_lowerCamelCase		,    _lowerCamelCase)
							plt.ylim(max([-80, bounds[0]])		,    min([80, bounds[1]]))
							plt.ylabel("Gain (dB)")

							plt.plot(_lowerCamelCase)
							plt.show()




def       _SCREAMING_SNAKE_CASE   (						_lowerCamelCase					:     FilterType		,    _lowerCamelCase					:     int)   ->       None:






							'''simple docstring'''





							__UpperCamelCase      :  Any       =   512
							__UpperCamelCase      :  Dict       =   [1] + [0] * (size - 1)
							__UpperCamelCase      :  Tuple       =   [filter_type.process(_lowerCamelCase) for item in inputs]

							__UpperCamelCase      :  Dict       =   [0] * (samplerate - size)  # zero-padding
							outputs += filler
							__UpperCamelCase      :  Optional[int]       =   np.angle(np.fft.fft(_lowerCamelCase))

							# Frequencies on log scale from 24 to nyquist frequency
							plt.xlim(24		,    samplerate / 2 - 1)
							plt.xlabel("Frequency (Hz)")
							plt.xscale("log")

							plt.ylim(-2 * pi		,    2 * pi)
							plt.ylabel("Phase shift (Radians)")
							plt.plot(np.unwrap(_lowerCamelCase		,    -2 * pi))
							plt.show()